# -*- coding: utf-8 -*-
"""
Class definition of YOLO_v3 style detection model on image and video
"""


import colorsys
import os
from timeit import default_timer as timer

import tensorflow as tf
import keras

import numpy as np
from keras import backend as K
from keras.models import load_model
from keras.layers import Input
from PIL import Image, ImageFont, ImageDraw
from PIL import Image as pImage
from yolo3.model import yolo_eval, yolo_body, tiny_yolo_body
from yolo3.utils import letterbox_image
from keras.utils import multi_gpu_model
from random import choices


# os.environ["CUDA_VISIBLE_DEVICES"] = str(choices([0,1])[0])

config = tf.ConfigProto()
# config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.05
keras.backend.tensorflow_backend.set_session(tf.Session(config=config))

class YOLO(object):

    _defaults = {
    "model_path": 'model_data/2019-9-28.h5',
    "labels": 'dog,cat',
    "score" : 0.3,
    "iou" : 0.45,
    "model_image_size" : (416, 416),
    "gpu_num" : 1,
    }

    @classmethod
    def get_defaults(cls, n):
        if n in cls._defaults:
            return cls._defaults[n]
        else:
            return "Unrecognized attribute name '" + n + "'"

    def __init__(self, **kwargs):
        self.__dict__.update(self._defaults) # set up default values
        self.__dict__.update(kwargs) # and update with user overrides
        self.class_names = self._get_class()
        self.anchors = self._get_anchors()
        self.sess = K.get_session()
        self.boxes, self.scores, self.classes = self.generate()

    def _get_class(self):
        # classes_path = os.path.expanduser(self.classes_path)
        # with open(classes_path) as f:
        #     class_names = f.readlines()
        class_names = self.labels
        class_names = [c.strip() for c in class_names.split(",")]
        return class_names

    def _get_anchors(self):
        anchors = "10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326"
        anchors = [float(x) for x in anchors.split(',')]
        return np.array(anchors).reshape(-1, 2)

    def generate(self):
        model_path = os.path.expanduser(self.model_path)
        assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'

        # Load model, or construct model and load weights.
        num_anchors = len(self.anchors)
        num_classes = len(self.class_names)
        is_tiny_version = num_anchors==6 # default setting
        try:
            self.yolo_model = load_model(model_path, compile=False)
        except:
            self.yolo_model = tiny_yolo_body(Input(shape=(None,None,3)), num_anchors//2, num_classes) \
                if is_tiny_version else yolo_body(Input(shape=(None,None,3)), num_anchors//3, num_classes)
            self.yolo_model.load_weights(self.model_path) # make sure model, anchors and classes match
        else:
            assert self.yolo_model.layers[-1].output_shape[-1] == \
                num_anchors/len(self.yolo_model.output) * (num_classes + 5), \
                'Mismatch between model and given anchor and class sizes'

        print('{} model, anchors, and classes loaded.'.format(model_path))

        # Generate colors for drawing bounding boxes.
        hsv_tuples = [(x / len(self.class_names), 1., 1.)
                      for x in range(len(self.class_names))]
        self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
        self.colors = list(
            map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
                self.colors))
        np.random.seed(10101)  # Fixed seed for consistent colors across runs.
        np.random.shuffle(self.colors)  # Shuffle colors to decorrelate adjacent classes.
        np.random.seed(None)  # Reset seed to default.

        # Generate output tensor targets for filtered bounding boxes.
        self.input_image_shape = K.placeholder(shape=(2, ))
        if self.gpu_num>=2:
            self.yolo_model = multi_gpu_model(self.yolo_model, gpus=self.gpu_num)
        boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors,
                len(self.class_names), self.input_image_shape,
                score_threshold=self.score, iou_threshold=self.iou)
        return boxes, scores, classes

    def detect_image(self, path):
        start = timer()
        result = []
        image = Image.open(path)

        if self.model_image_size != (None, None):
            assert self.model_image_size[0]%32 == 0, 'Multiples of 32 required'
            assert self.model_image_size[1]%32 == 0, 'Multiples of 32 required'
            boxed_image = letterbox_image(image, tuple(reversed(self.model_image_size)))
        else:
            new_image_size = (image.width - (image.width % 32),
                              image.height - (image.height % 32))
            boxed_image = letterbox_image(image, new_image_size)
        image_data = np.array(boxed_image, dtype='float32')

        print(image_data.shape)
        image_data /= 255.
        image_data = np.expand_dims(image_data, 0)  # Add batch dimension.

        out_boxes, out_scores, out_classes = self.sess.run(
            [self.boxes, self.scores, self.classes],
            feed_dict={
                self.yolo_model.input: image_data,
                self.input_image_shape: [image.size[1], image.size[0]],
                K.learning_phase(): 0
            })

        print('Found {} boxes for {}'.format(len(out_boxes), 'img'))

        # font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
        #             size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
        # thickness = (image.size[0] + image.size[1]) // 300
        
        for i, c in reversed(list(enumerate(out_classes))):
            predicted_class = self.class_names[c]
            box = out_boxes[i]
            score = out_scores[i]

            label = '{} {:.2f}'.format(predicted_class, score)
            # draw = ImageDraw.Draw(image)
            # label_size = draw.textsize(label, font)

            top, left, bottom, right = box
            top = max(0, np.floor(top + 0.5).astype('int32'))
            left = max(0, np.floor(left + 0.5).astype('int32'))
            bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))
            right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
            print(label, (left, top), (right, bottom))
            result.append([label, (left, top), (right, bottom)])
            ########################################################3
            # if top - label_size[1] >= 0:
            #     text_origin = np.array([left, top - label_size[1]])
            # else:
            #     text_origin = np.array([left, top + 1])

            # # My kingdom for a good redistributable image drawing library.
            # for i in range(thickness):
            #     draw.rectangle(
            #         [left + i, top + i, right - i, bottom - i],
            #         outline=self.colors[c])
            # draw.rectangle(
            #     [tuple(text_origin), tuple(text_origin + label_size)],
            #     fill=self.colors[c])
            # draw.text(text_origin, label, fill=(0, 0, 0), font=font)
            # del draw

        end = timer()
        print(end - start)
        return image,result

    def close_session(self):
        self.sess.close()



